Feature Extraction of Rolling Bearings Based on the Combination of New Wavelet Threshold Method and VMD
For the problems of rolling bearing fault signals such as weak signal intensity and difficulty in extracting,a new wave-let threshold combined with VMD fault signal feature extraction method is proposed.Firstly,a new exponential wavelet threshold function is used to improve the traditional wavelet denoising method and the problems of discontinuous points and constant devia-tions are overcame;Secondly,the variational modal decomposition(VMD)is used to extract the effective fault features of the roll-ing bearing;Finally,the vibration data of the 6205-Rs bearing inner ring failure is used for experimental verification.The exper-imental results show that the proposed method can effectively improve the signal-to-noise ratio(SNR)of noise reduction signals and reduce the root mean square error(RMSE),and the completeness and effectiveness of the fault feature extraction of rolling bearings can be guaranteed.
Rolling BearingNew Wavelet ThresholdVariational Modal DecompositionFeature Extraction
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